The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Artificial intelligence and machine learning in healthcare apply computational methods to clinical and biomedical data to support diagnosis, treatment planning, and health system operations. Common applications include medical image analysis using convolutional neural networks to detect tumors, fractures, and other abnormalities in radiology and pathology images; predictive models that estimate patient risk for readmission, sepsis, or disease progression from electronic health records; natural language processing for extracting information from clinical notes; and drug discovery models that predict molecular properties and candidate compounds. Regulatory activity reflects this growth: the FDA had authorized more than 1,300 AI-enabled medical devices by December 2025, with a record 295 cleared that year alone. Deploying AI in clinical settings raises distinct requirements around model interpretability for clinician trust, validation across diverse patient populations, and integration with existing hospital information systems. As an open-access healthcare AI journal, IJACSA publishes research on AI and machine learning models in healthcare, their clinical validation, and applied systems for diagnostic support and patient monitoring.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
Multi-sensor data in medical monitoring includes waveform changes in physiological signals and time-series characteristics of disease progression. These features typically exhibit high-dimensionality, large-scale, and ti…
The integration of IoT in healthcare has remained very dynamic, with a lot of improvement in the health of patients and the running of operations. Integration also comes with new risks and threats, raising IoT healthcare…
Dengue fever continues to be a significant public health issue across the globe because it can lead to life-threatening complications. Severity prediction in a timely and precise manner is imperative for proper clinical…
According to international organizations’ statistics, the percentage of disabled people is considered not just a small percentage of the world's population. Improving the quality of life for people by using new technolog…
In resource-limited settings, assisting physicians with disease identification can significantly improve patient outcomes. Early diagnosis is crucial, as many patients could remain healthy with timely intervention. Recen…
In recent years, the healthcare sector has faced growing challenges in managing patient data securely and efficiently, especially when it comes to data privacy and the way information is shared across healthcare provider…
Radiological bone age assessment is essential for diagnosing pediatric growth and developmental disorders. The conventional Greulich-Pyle Atlas, though widely used, is manual, time-intensive, and prone to inter-observer…
Federated learning (FL) is a cutting-edge method of collaborative machine learning that lets organizations or companies train models without exchanging personal information. Adversarial attacks such as data poisoning, mo…
To that end, this study presents the Hierarchical Context-Aware Transformer (HCAT), a new model to perform analysis on unstructured healthcare data that resolves significant problems related to medical text. In the propo…
Accurate disease prediction from symptom descriptions is vital for improving early detection and enabling remote healthcare services, especially in the evolving landscape of digital health. Traditional diagnosis methods…
